Omics AI MCP Server

A Model Context Protocol (MCP) server that enables AI agents to conversationally interact with Omics AI Explorer networks for genomics research and data analysis.

Overview

This MCP server wraps the functionality of the omics-ai-python-library to provide AI agents with seamless access to genomics data across multiple Omics AI Explorer networks including:

• HiFi Solves (hifisolves.org) - Long-read sequencing data
• Neuroscience AI (neuroscience.ai) - Neuroscience genomics data
• ASAP (cloud.parkinsonsroadmap.org) - Aligning Science Across Parkinson's
• Viral AI (viral.ai) - Viral genomics and surveillance data
• Target ALS (dataportal.targetals.org) - ALS research data

Features

The MCP server provides the following tools for AI agents:

• `list_collections` - Discover available data collections in any network
• `list_tables` - Browse tables within specific collections
• `get_schema_fields` - Examine table schemas and field types
• `query_table` - Query data with filters, pagination, and ordering
• `count_rows` - Count rows matching specific criteria
• `sql_search` - Execute direct SQL queries using Trino syntax

Installation

git clone https://github.com/mfiume/omics-ai-mcp.git
cd omics-ai-mcp
npm install

Usage

Running the Server

npm start

Configuration for Claude Desktop

Add to your Claude Desktop configuration file:

{
  "mcpServers": {
    "omics-ai": {
      "command": "node",
      "args": ["src/index.js"],
      "cwd": "/path/to/omics-ai-mcp"
    }
  }
}

Example AI Agent Interactions

Once configured, AI agents can conversationally query genomics data:

> Agent: "What collections are available on the Viral AI network?"

> User: "Show me the tables in the virusseq collection"

> Agent: "List the first 10 variants from the variants table where chromosome equals chr1"

> User: "How many total variants are in the VirusSeq database?"

Tool Reference

list_collections

Lists all available collections in a network.

Parameters:

• `network` (required): Network name (hifisolves, viral, neuroscience, etc.) or full URL
• `access_token` (optional): Authentication token

list_tables

Lists all tables in a specific collection.

Parameters:

• `network` (required): Network name or URL
• `collection_slug` (required): Collection identifier (e.g., "virusseq", "gnomad")
• `access_token` (optional): Authentication token

get_schema_fields

Retrieves the schema and field definitions for a table.

Parameters:

• `network` (required): Network name or URL
• `collection_slug` (required): Collection identifier
• `table_name` (required): Qualified table name (e.g., "collections.virusseq.variants")
• `access_token` (optional): Authentication token

query_table

Queries data from a table with optional filtering and pagination.

Parameters:

• `network` (required): Network name or URL
• `collection_slug` (required): Collection identifier
• `table_name` (required): Qualified table name
• `filters` (optional): Filter criteria object
• `limit` (optional): Max rows to return (default: 100)
• `offset` (optional): Rows to skip (default: 0)
• `order_by` (optional): Sort specification
• `access_token` (optional): Authentication token

count_rows

Counts rows matching specified filters.

Parameters:

• `network` (required): Network name or URL
• `collection_slug` (required): Collection identifier
• `table_name` (required): Qualified table name
• `filters` (optional): Filter criteria object
• `access_token` (optional): Authentication token

sql_search

Executes direct SQL queries against a collection using Trino syntax with enhanced result formatting.

Parameters:

• `network` (required): Network name or URL
• `collection_slug` (required): Collection identifier
• `sql` (required): SQL query string (use Trino syntax with double quotes for identifiers)
• `max_polls` (optional): Maximum number of polling attempts (default: 10)
• `poll_interval` (optional): Seconds to wait between polls (default: 2.0)
• `access_token` (optional): Authentication token

Features:

• Automatic async query polling with robust error handling
• Intelligent result formatting (table view for simple data, JSON for complex)
• Progress indicators and pagination info
• Timeout management for long-running queries

Example queries:

• `SELECT COUNT(*) FROM "schema"."table"`
• `SELECT * FROM "schema"."variants" WHERE chrom = 'chr1' LIMIT 10`
• `SELECT DISTINCT chrom FROM "schema"."variants" ORDER BY chrom`

Supported Networks

The server supports these pre-configured networks (use short names for convenience):

Error Handling

The server includes robust error handling for:

• Network connectivity issues
• Authentication failures
• Invalid parameters
• API response parsing errors
• Timeout handling for long-running queries

Development

# Development mode with auto-restart
npm run dev

# Production mode
npm start

License

MIT License - see LICENSE file for details.

Related Projects

• omics-ai-python-library - Python client library
• Model Context Protocol - MCP specification and SDKs